UNICEF Data Story: Global Child Well-being through DTP Vaccination Analysis

1 Executive Summary

This report analyzes global disparities in DTP (Diphtheria, Tetanus, and Pertussis) vaccination coverage using UNICEF data. It explores vaccination trends, top-performing countries, and the influence of national income (GNI per capita) on healthcare outcomes.

Key findings reveal:

• Significant regional inequalities in vaccine access.
• A positive correlation between higher income and better immunization rates.
• The need for targeted healthcare interventions in low-coverage regions.

The analysis supports strategic actions aligned with SDG 3 (Good Health and Well-Being) to ensure equitable vaccine distribution globally.

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2 Introduction

Vaccination remains one of the most effective public health strategies to safeguard child health. Among vaccines, DTP coverage is widely recognized as a key indicator of healthcare system strength and equity.

This report examines global DTP vaccination rates by analyzing UNICEF’s latest child health dataset. It also explores the relationship between economic prosperity and immunization coverage to identify patterns, disparities, and opportunities for improvement.

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3 Data Setup

# Import required libraries
import pandas as pd
import plotly.express as px
import statsmodels.api as sm

# Load the UNICEF dataset
unicef = pd.read_csv("Unicef.csv")

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4 Methodology

We extracted and analyzed DTP vaccination coverage indicators, complemented by economic metrics (GNI per capita), to uncover patterns and disparities across countries and over time.

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5 DTP Vaccination Data Preparation

# Filter DTP-related indicators
dtp_data = unicef[unicef['indicator'].str.contains('DTP', case=False, na=False)]

# Select the latest available record per country
dtp_latest = (
    dtp_data.sort_values('time_period', ascending=False)
             .drop_duplicates(subset='country')
)[['country', 'obs_value']].rename(columns={'obs_value': 'DTP_Coverage'})

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6 Analysis and Visualizations

6.1 Global Distribution of DTP Vaccination Coverage

fig_map = px.choropleth(
    dtp_latest,
    locations="country",
    locationmode="country names",
    color="DTP_Coverage",
    color_continuous_scale="Viridis",
    title="Global DTP Vaccination Coverage (%)",
    labels={'DTP_Coverage': 'Coverage (%)'},
    width=750,        # Fixed width for no scroll
    height=500,       # Good height for clarity
    projection="natural earth"   # Better global fitting view
)

# Fine-tune margins
fig_map.update_layout(
    margin=dict(l=0, r=0, t=50, b=0),  # remove left/right margins
    coloraxis_colorbar=dict(
        title="Coverage (%)",          # Color bar title
        ticks="outside"                # Ticks outside for neatness
    )
)

fig_map.show()

Insight:
Coverage varies greatly, with Sub-Saharan Africa and parts of South Asia showing lower immunization rates.

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6.2 DTP Vaccination Trends Over Time for Selected Countries

sample_countries = ['India', 'Nigeria', 'Brazil', 'United States of America', 'South Africa']
dtp_sample = dtp_data[dtp_data['country'].isin(sample_countries)]

fig_line = px.line(
    dtp_sample,
    x='time_period',
    y='obs_value',
    color='country',
    markers=True,
    title="DTP Vaccination Trends Over Time",
    labels={'obs_value': 'DTP Coverage (%)', 'time_period': 'Year'},
    width=750,
    height=500
)
fig_line.show()

Insight:
India and South Africa have shown steady improvement, while Brazil and the USA maintain high stability.

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6.3 Top 10 Countries with Highest DTP Coverage

top10 = dtp_latest.nlargest(10, 'DTP_Coverage')

fig_bar = px.bar(
    top10,
    x='DTP_Coverage',
    y='country',
    orientation='h',
    color='DTP_Coverage',
    color_continuous_scale='Cividis',
    title="Top 10 Countries by DTP Coverage",
    labels={'DTP_Coverage': 'Coverage (%)'},
    width=800,
    height=450
)

fig_bar.update_layout(
    yaxis=dict(autorange="reversed"),
    bargap=0.4
)

fig_bar.show()

Insight:
Top-performing countries achieve immunization rates near 100%, highlighting effective healthcare programs.

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6.4 GNI per Capita vs DTP Vaccination Coverage

gni_data = unicef[unicef['indicator'].str.contains('GNI', case=False, na=False)]

gni_latest = (
    gni_data.sort_values('time_period', ascending=False)
             .drop_duplicates(subset='country')
)[['country', 'obs_value']].rename(columns={'obs_value': 'GNI_per_capita'})

merged = pd.merge(dtp_latest, gni_latest, on='country', how='inner')

fig_scatter = px.scatter(
    merged,
    x='GNI_per_capita',
    y='DTP_Coverage',
    trendline='ols',
    color='DTP_Coverage',
    color_continuous_scale='Turbo',
    title="GNI per Capita vs DTP Vaccination Coverage",
    labels={
        'GNI_per_capita': 'GNI per Capita (USD)',
        'DTP_Coverage': 'DTP Coverage (%)'
    },
    width=700,
    height=500
)
fig_scatter.update_traces(marker=dict(size=7, opacity=0.8))
fig_scatter.show()

Insight:
Higher income tends to correspond with better vaccination coverage, but governance also plays a key role.

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7 Conclusion

This study highlights global disparities in DTP vaccination coverage, linked closely to national income levels and healthcare system strength. Public health interventions and equitable healthcare investments remain critical to achieving SDG 3 goals globally.

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8 References

• UNICEF (2025). Immunization and Child Health Data.
• World Bank (2025). Global GNI per Capita Statistics.

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